| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 40 | | tagDensity | 0.325 | | leniency | 0.65 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1453 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1453 | | totalAiIsms | 30 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | |
| | highlights | | 0 | "gloom" | | 1 | "shattered" | | 2 | "tracing" | | 3 | "porcelain" | | 4 | "etched" | | 5 | "traced" | | 6 | "pulsed" | | 7 | "magnetic" | | 8 | "warmth" | | 9 | "pulse" | | 10 | "flickered" | | 11 | "echoed" | | 12 | "quivered" | | 13 | "silence" | | 14 | "whisper" | | 15 | "silk" | | 16 | "trembled" | | 17 | "vibrated" | | 18 | "footfall" | | 19 | "throbbed" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 157 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 157 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 184 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1446 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 55 | | wordCount | 1155 | | uniqueNames | 10 | | maxNameDensity | 1.82 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 21 | | Camden | 1 | | Tube | 1 | | Ishaan | 1 | | Patel | 14 | | Morris | 1 | | Kowalski | 1 | | Eva | 11 | | Smoke | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Ishaan" | | 3 | "Patel" | | 4 | "Morris" | | 5 | "Kowalski" | | 6 | "Eva" | | 7 | "Smoke" |
| | places | (empty) | | globalScore | 0.591 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 96 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1446 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 184 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 48 | | mean | 30.13 | | std | 17.4 | | cv | 0.577 | | sampleLengths | | 0 | 54 | | 1 | 56 | | 2 | 33 | | 3 | 47 | | 4 | 11 | | 5 | 26 | | 6 | 7 | | 7 | 75 | | 8 | 56 | | 9 | 9 | | 10 | 26 | | 11 | 31 | | 12 | 41 | | 13 | 9 | | 14 | 9 | | 15 | 39 | | 16 | 12 | | 17 | 63 | | 18 | 7 | | 19 | 43 | | 20 | 9 | | 21 | 30 | | 22 | 15 | | 23 | 34 | | 24 | 12 | | 25 | 48 | | 26 | 32 | | 27 | 35 | | 28 | 17 | | 29 | 25 | | 30 | 36 | | 31 | 39 | | 32 | 25 | | 33 | 31 | | 34 | 20 | | 35 | 45 | | 36 | 9 | | 37 | 3 | | 38 | 40 | | 39 | 22 | | 40 | 11 | | 41 | 56 | | 42 | 7 | | 43 | 42 | | 44 | 50 | | 45 | 37 | | 46 | 41 | | 47 | 21 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 157 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 229 | | matches | (empty) | |
| 96.27% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 184 | | ratio | 0.016 | | matches | | 0 | "Bone token design — something sold only at the underground market." | | 1 | "Threads of memory snagged at her mind—Morris’s last case, an abandoned dockyard, the same symbol carved into a shattered door." | | 2 | "The puddle rippled, then receded—like a tide pulling back." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1161 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 25 | | adverbRatio | 0.02153316106804479 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.002583979328165375 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 184 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 184 | | mean | 7.86 | | std | 4.78 | | cv | 0.609 | | sampleLengths | | 0 | 19 | | 1 | 11 | | 2 | 8 | | 3 | 4 | | 4 | 12 | | 5 | 8 | | 6 | 13 | | 7 | 11 | | 8 | 11 | | 9 | 5 | | 10 | 8 | | 11 | 7 | | 12 | 9 | | 13 | 17 | | 14 | 6 | | 15 | 13 | | 16 | 7 | | 17 | 10 | | 18 | 11 | | 19 | 3 | | 20 | 8 | | 21 | 4 | | 22 | 7 | | 23 | 3 | | 24 | 8 | | 25 | 4 | | 26 | 6 | | 27 | 1 | | 28 | 6 | | 29 | 13 | | 30 | 9 | | 31 | 7 | | 32 | 11 | | 33 | 20 | | 34 | 9 | | 35 | 15 | | 36 | 6 | | 37 | 7 | | 38 | 12 | | 39 | 16 | | 40 | 4 | | 41 | 3 | | 42 | 2 | | 43 | 3 | | 44 | 8 | | 45 | 15 | | 46 | 16 | | 47 | 4 | | 48 | 3 | | 49 | 2 |
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| 46.20% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.29347826086956524 | | totalSentences | 184 | | uniqueOpeners | 54 | |
| 23.15% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 144 | | matches | | 0 | "Then came a metallic sigh," |
| | ratio | 0.007 | |
| 72.78% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 53 | | totalSentences | 144 | | matches | | 0 | "Her torch sliced through gloom," | | 1 | "She paused at the platform’s" | | 2 | "She leaned closer, inhaled copper" | | 3 | "He aimed at chalk-scrawled outlines" | | 4 | "She nudged one with her" | | 5 | "It cracked under pressure, fine" | | 6 | "She studied the shards." | | 7 | "They gleamed pale against the" | | 8 | "She nudged another fragment" | | 9 | "It splintered like porcelain." | | 10 | "She traced one rune with" | | 11 | "It felt cold as ice," | | 12 | "Her partner lost to forces" | | 13 | "She rose, flashlight illuminating her" | | 14 | "She shone light on the" | | 15 | "She ignored him." | | 16 | "She flipped it open, caught" | | 17 | "She lifted her head." | | 18 | "Her curls caught the light" | | 19 | "She knelt by the runes," |
| | ratio | 0.368 | |
| 5.14% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 131 | | totalSentences | 144 | | matches | | 0 | "Detective Harlow Quinn’s boot crunched" | | 1 | "Her torch sliced through gloom," | | 2 | "A thin hiss whispered from" | | 3 | "She paused at the platform’s" | | 4 | "A man in a navy" | | 5 | "Quinn crouched, beam trembling over" | | 6 | "The stain bubbled like oil." | | 7 | "She leaned closer, inhaled copper" | | 8 | "He aimed at chalk-scrawled outlines" | | 9 | "Quinn edged closer to the" | | 10 | "A cluster of bone fragments" | | 11 | "She nudged one with her" | | 12 | "It cracked under pressure, fine" | | 13 | "Patel glanced over." | | 14 | "She studied the shards." | | 15 | "They gleamed pale against the" | | 16 | "She nudged another fragment" | | 17 | "It splintered like porcelain." | | 18 | "Patel swallowed, scribbled in his" | | 19 | "Quinn’s beam swept the tunnel" |
| | ratio | 0.91 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 144 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 45 | | technicalSentenceCount | 2 | | matches | | 0 | "She muttered under her breath, matching glyphs to runes, eyes darting." | | 1 | "The compass needle beat against its casing, as though eager to be free." |
| |
| 86.54% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 1 | | matches | | 0 | "she muttered, voice low" |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 40 | | tagDensity | 0.125 | | leniency | 0.25 | | rawRatio | 0.2 | | effectiveRatio | 0.05 | |